Local-Llm

TL;DR Best entry point: M2 Max 32-64 GB (~£1.4k-£2k) for 7B-13B models at 25-40 tok/s Best sweet spot: M2 Ultra 64-128 GB (~£3k-£4.5k) handles 30B+ models comfortably Best for 70B models: M3 Ultra 128 GB+ (~£5.5k+) with 800+ GB/s bandwidth Newer alternative: M4 Max (£2k-£4k) - lower bandwidth (410-546 GB/s) than Ultra chips, but still solid for 7B-13B models Key rule: Memory bandwidth matters more than raw compute for token generation Reality check: A RTX 5090 rig is 2-3× faster for similar money - buy Mac for simplicity and unified memory You want to run large language models locally on a Mac Studio. Good idea - unified memory is genuinely useful for LLMs. But the specs matter, and there are some hard truths about what “works” versus what feels responsive. More importantly: the right Mac depends entirely on which model you want to run. ...

By early 2026, the “Local vs. Cloud” debate has moved past the experimental phase. We are no longer just “trying to see if Llama runs on a Mac.” Instead, professional engineers are building sophisticated Hybrid AI Stacks where local and cloud models work in tandem. The landscape has shifted because the hardware caught up to the software. With the prevalence of unified memory on Apple Silicon and the accessibility of 24GB+ VRAM cards like the RTX 50-series, the “local” ceiling has been smashed. ...